A thorough understanding of visual information processing is important not only because vision is the largest source of sensory input to the nervous system, but also because the visual system is a model for neural information processing in general. The main proposed research (1) is a novel approach to the understanding of complex visual processing and how the submodalities of vision interact. In previous work, we have developed a class of visual stimuli ("isodipole textures", Julesz et. al. 1978) and associated evoked-potential (VEP) and psychophysical paradigms which separate linear and simple nonlinear processes from more complex nonlinear interactions. Modifications of this technique, employing statistically-degraded textures, will be used to define the spatial scale of the complex interactions that subserve pattern processing ((1.11)). The internal spatial organization of these mechanisms will be probed by stimuli based on new but related classes of isodipole textures ((1.2) and (1.6)). The dynamics of these mechanisms will be measured and modelled by time- and frequency-domain methods of nonlinear systems analysis ((1.8)). The role of chromatic channels in form processing will be studied with extensions of this techniques to chromatic stimuli ((1.3) and (1.4)); this will also allow a distinction between chromatic contrast and other forms of chromatic information. The interaction of motion processing and form processing will be investigated with dynamic isodipole textures ((1.5)). The scalp topography of the VEP ((1.7)), as well as patients with defined lesions ((1.10)), will be analyzed for clues to the anatomical localization of complex processing in the several visual submodalities. Theoretical efforts concerning further development of this and related techniques are planned ((1.9)). The minor project (2) is an analysis of the relationship between the VEP and the electroencephalogram (EEG). If, as is widely assumed, VEP signal and EEG noise combine addictively, and improvement (the Tcirc 2-statistic) over currently-used procedures for estimation of steady-state responses and their confidence limits is possible. We plan to rigorously check for such adaptivity ((2.1)), and to compare our new statistical approach with the standard methods of phase-coherence and the generalized T2-test ((2.2)).